Ensuring Comprehensive Responses Across All Questions by chain

[mraguth]

Currently, my chain is only answering the first question. How can I make it work so that all questions are addressed? Or better ways other than fuzzy_match_runnable?

from langchain.chains import create_citation_fuzzy_match_runnable
from langchain_openai import ChatOpenAI

query = """ Execute these questions one after one and write answers based on your end results.

1. **Strategic Leadership**: Define and execute a global vision and technical strategy for AI & Gen AI services that aligns with company goals and market trends.
2. **Team Management**: Mentor and manage a multidisciplinary team of data scientists, machine learning engineers, and Gen AI professionals, fostering a culture of innovation and continuous improvement.

"""

context = data             ##### bundle of text data
question = query

chain = create_citation_fuzzy_match_runnable(llm)
answer = chain.invoke({"question": question, "context": context})

[dosubot[bot]]

I found a similar unsolved discussion regarding the ainvoke() function missing a required positional argument, which might be related to handling multiple questions ^[1].

To address your issue of ensuring comprehensive responses across all questions, you can consider using a query decomposition technique. This involves breaking down your complex input into simpler sub-questions that can be processed individually. Here's an example of how you can achieve this using LangChain:

from pydantic import BaseModel, Field
from langchain_openai import ChatOpenAI
from langchain_core.messages import SystemMessage, HumanMessage

# Define a pydantic model to enforce the output structure
class Questions(BaseModel):
    questions: List[str] = Field(
        description="A list of sub-questions related to the input query."
    )

# Create an instance of the model and enforce the output structure
model = ChatOpenAI(model="gpt-4o", temperature=0) 
structured_model = model.with_structured_output(Questions)

# Define the system prompt
system = """You are a helpful assistant that generates multiple sub-questions related to an input question. \n
The goal is to break down the input into a set of sub-problems / sub-questions that can be answered in isolation. \n"""

# Pass the question to the model
question = """Execute these questions one after one and write answers based on your end results.

1. **Strategic Leadership**: Define and execute a global vision and technical strategy for AI & Gen AI services that aligns with company goals and market trends.
2. **Team Management**: Mentor and manage a multidisciplinary team of data scientists, machine learning engineers, and Gen AI professionals, fostering a culture of innovation and continuous improvement.
"""

questions = structured_model.invoke([SystemMessage(content=system)]+[HumanMessage(content=question)])

This code uses a structured output model to decompose your input into sub-questions, which can then be processed sequentially or in parallel. This approach might be more effective than using fuzzy_match_runnable for handling multiple questions ^[2].

To continue talking to Dosu, mention @dosu.

---

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[mraguth]

@dosu it is not working. Is that possible to use prompt to instruct the requirement and pass that in the answer chain? thoughts?

[dosubot[bot]]

Yes, it is possible to use a prompt to instruct the requirement and pass that in the answer chain to ensure comprehensive responses across all questions. The LangChain framework provides mechanisms for this through the use of PromptTemplate and ConditionalPromptSelector. These tools allow you to define prompts that guide the response generation process, ensuring that all questions are addressed comprehensively. You can use ConditionalPromptSelector to select different prompts based on specific conditions, such as whether a chat model is being used, to ensure the appropriate prompt is applied for generating responses ^[1].

To continue talking to Dosu, mention @dosu.

---

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[mraguth]

@dosu can you hep me how I add prompt template in the below code?

from langchain.chains import create_citation_fuzzy_match_runnable
from langchain_openai import ChatOpenAI

query = """ Execute these questions one after one and write answers based on your end results.

1. **Strategic Leadership**: Define and execute a global vision and technical strategy for AI & Gen AI services that aligns with company goals and market trends.
2. **Team Management**: Mentor and manage a multidisciplinary team of data scientists, machine learning engineers, and Gen AI professionals, fostering a culture of innovation and continuous improvement.

"""

context = data             ##### bundle of text data
question = query

chain = create_citation_fuzzy_match_runnable(llm)
answer = chain.invoke({"question": question, "context": context})

[dosubot[bot]]

To add a prompt template to your code using create_citation_fuzzy_match_runnable, you can integrate a ChatPromptTemplate as part of the chain. Here's how you can modify your code to include a prompt template:

from langchain.chains import create_citation_fuzzy_match_runnable
from langchain_openai import ChatOpenAI
from langchain_core.messages import SystemMessage, HumanMessagePromptTemplate
from langchain_core.prompts import ChatPromptTemplate

# Define your language model
llm = ChatOpenAI(model="gpt-4o", temperature=0)

# Create a prompt template
prompt = ChatPromptTemplate(
    [
        SystemMessage(
            "You are a world class algorithm to answer "
            "questions with correct and exact citations."
        ),
        HumanMessagePromptTemplate.from_template(
            "Answer question using the following context."
            "\n\n{context}"
            "\n\nQuestion: {question}"
            "\n\nTips: Make sure to cite your sources, "
            "and use the exact words from the context."
        ),
    ]
)

# Create the chain with the prompt template
chain = create_citation_fuzzy_match_runnable(llm)

# Invoke the chain with your question and context
answer = (prompt | chain).invoke({"question": question, "context": context})

In this setup, the ChatPromptTemplate is used to define a structured prompt that includes a SystemMessage and a HumanMessagePromptTemplate. This template is then combined with the language model to create a Runnable that processes the input and generates responses with structured citations ^[1].

To continue talking to Dosu, mention @dosu.

---

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[mraguth]

Error "---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
Cell In[53], line 3
1 from langchain.chains import create_citation_fuzzy_match_runnable
2 from langchain_openai import ChatOpenAI
----> 3 from langchain_core.messages import SystemMessage, HumanMessagePromptTemplate
4 from langchain_core.prompts import ChatPromptTemplate
6 # Define your language model

ImportError: cannot import name 'HumanMessagePromptTemplate' from 'langchain_core.messages' (c:\Users\Moha_Murali.conda\envs\langchain\Lib\site-packages\langchain_core\messages_init_.py)"